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Associations between cerebral perfusion pressure, hemodynamic parameters, and cognitive test values in normal-tension glaucoma patients, Alzheimer’s disease patients, and healthy controls /
No full textBackground/Objectives: Glaucoma and Alzheimer’s disease (AD) are neurodegenerative conditions with vascular underpinnings. This study aimed to explore the relationship between blood pressure parameters such as mean arterial pressure (MAP), pulse pressure (PP), and cerebral perfusion pressure (CPP) and cognitive performance in patients with AD, normal-tension glaucoma (NTG), and healthy controls. We hypothesized that NTG patients, like those with mild cognitive impairment (MCI), may experience subtle cognitive changes related to vascular dysregulation. Methods: Ninety-eight participants (35 NTG, 17 AD, 46 controls) were assessed for CPP, MAP, OPP, and cognitive performance. Statistical analyses compared groups and examined correlations. Results: AD patients showed lower CPP and MAP (p < 0.001), indicating systemic vascular dysfunction, while NTG patients had higher ocular perfusion pressure (OPP) (p = 0.008), suggesting compensatory mechanisms. CPP correlated with visuospatial abilities in AD (r = 0.492, p = 0.045). MAP correlated with the Clock drawing test (CDT) scores in the NTG group (r = 0.378, p = 0.025). PP negatively correlated with cognition in AD (r = −0.527, p = 0.016 for CDT scores) and controls (r = −0.440, p = 0.002 for verbal fluency and r = −0.348, p = 0.019 for total ACE scores). Conclusions: The study highlights distinct hemodynamic profiles: systemic dysfunction in AD and localized dysregulation in NTG. These findings emphasize the role of vascular dysregulation in neurodegeneration, with implications for personalized treatment approaches targeting vascular health in neurodegenerative conditions
The interrelationship between wages and labour productivity in the Lithuanian industrial sector.
No full textRelevance. The interrelationship between wages and labour productivity is one of the fundamental foundations of the economic system, especially in the Lithuanian industrial sector, which accounts for a significant part of GDP. In today’s economy, wage and labour productivity indicators are closely linked, influenced by rising social expectations, inflationary pressure, and competition. Uneven and disproportionate growth in wage and labour productivity indicators can lead to economic imbalances, reduce the sector’s competitiveness and, in the long run, affect the national economy. The final thesis aims to clarify how wages and labour productivity interact in the subsectors of Lithuanian industry (mining and quarrying (B), manufacturing (C), electricity, gas, steam supply and air conditioning (D), water supply, wastewater treatment, waste management and regeneration (E)). Research object. The interrelationship between wages and labour productivity. Research aim. To assess the interrelationship between wages and labour productivity in the Lithuanian industrial sector. Project results. Lithuania’s labour productivity in 2023 was about 1,6 times (32,22 thousand euros) lower than the EU, and wages were 1,9 times (13,66 thousand euros) lower than the EU. To ensure the country’s economic growth and competitiveness, it is important to analyse the interrelationship between wages and labour productivity in the Lithuanian industrial sector. A review of the academic literature revealed a lack of consensus among researchers regarding the direction of the causal interrelationship between these indicators, which one serves as the cause and which is the effect. Additional factors that may impact the changes in these indicators were also identified, including the number of employees, hours worked, wages, industrial output, foreign investments, and labour productivity. An econometric assessment was conducted for the period 2008-2024. The results showed that: in the mining and quarrying (B) subsector there is a reciprocal interrelationship between labour productivity and wages, econometric research showed that wages are increasing faster than labour productivity; in the manufacturing (C) subsector, a causal interrelationship between these indicators was not established; in the electricity, gas, steam supply, and air conditioning (D) subsector, the labour productivity indicator affects wages, but an econometric model could not be created; in the wate supply, wastewater treatment, waste management, and regeneration (E) subsector, wages affect labour productivity, econometric research found that if wages increases by 1 euro, labour productivity would increase by approximately 0,65 euros. The interrelationship between wages and labour productivity in Lithuanian industry is not unambiguous – sometimes wages stimulate labour productivity, sometimes there is no relationship or it is the opposite. That indicates complex relationship influenced by additional factors
Analysis of defect detection probability in the adhesive bonds using non-destructive testing.
No full textThis project investigates probability of defect detection in the adhesive layer and identifies and investigates the parameters that affect the probability of detecting a defect. The CIVA 2021 software is used to create a model and study a bonded joint made from 2024 aluminium alloy plates. These two 1.8 mm thick plates are connected by an adhesive layer with a thickness of 0.16 mm. Importantly, due to the accuracy and speed of the software calculations, only the bonded joint, which is 25 mm wide and 280 mm long, is modelled. In the bonded joint, 9 square defects of three different sizes are modelled, with side lengths of: 5 mm, 10 mm, 15 mm. One defect of each size is positioned at different depths: 1,8 mm, 1,88 mm, 1,96 mm. The study is performed using a non-contact ultrasonic method. This means that the sample and transducer are immersed in a water tank. A computer-based comparison study of focused and unfocused transducers with a frequency of 15 MHz and diameter of 10 mm is performed, during which it is determined that the use of a focused transducer ensures that the signal will be reflected from the defect, and not from neighbour surfaces. The results of create computer model are verified by the experimental test. The experimental test is performed using a focused Olympus V328-SU-F transducer. A computer metamodel has been created, which is required for calculations, and consists of 1000 different combinations. Next, a computer-based sensitivity study of the focused transducer is performed, when various parameters such as the thickness of the sample layers, the size of the defect, and its depth position are changed, and it is estimated that the defect size and the position of the transducer have the greatest influence on the defect detectability. Sensitivity study is performed using Sobol indices and Kriging interpolation methods. It was found that both tests are most sensitive to the defect size and the position of the transducer relative to the x-axis of the sample. However, it has been observed that using an unfocused signal increases the sensitivity of the study to the angle of the transducer placement. An analysis of the probability of defect detection is also performed using the Monte Carlo method. The probability of detecting a defect with a 90% probability of 95% confidence limit using different types of transducers is analysed. It was observed that when the study performed with an unfocused transducer, a 90% probability of detecting a defect is not achieved. When using a focused transducer, this probability is achieved when the defect is not smaller than 3,57 mm, and with a 95% confidence limit defects are detected that are not less than 4,28 mm
Bepiločių orlaivių nuotolinės struktūrinės būklės stebėsenos sistemos kūrimas.
No full textThis study investigates the application of electrically conductive sandwich-structured composites with an aramid honeycomb core for damage monitoring in unmanned aerial vehicles (UAVs). Honeycomb cores are susceptible to failure modes such as buckling, wrinkling, and delamination, which are difficult to detect using conventional damage detection methods. Various techniques have been applied in the aerospace industry, including fibre optics, accelerometers, and piezoelectric sensors. Although effective in some applications, these systems are often expensive, complex, and have limited ability to detect internal damage within the honeycomb core. To address these limitations, this study explores the use of electrically conductive nanoparticles for structural health monitoring (SHM). When embedded into a composite, conductive networks exhibit a piezoresistive effect – mechanical damage leads to an increase in electrical resistance, which can be measured. Specimens consisted of an aramid honeycomb coated with MXene nanoparticles and six layers of glass fibre, one of which was covered with carbon nanotubes. Additionally, carbon fibre threads and unidirectional carbon fibre were integrated into the composite to measure local electrical resistance using a multimeter. Following electrical measurements of the individual components (honeycomb and glass fibre), an analytical model based on Ohm’s law was developed. The model demonstrated that the sensitivity of the electrical resistance of the composite depends on the relative conductivity of the individual layers. Composites were tested for compression, three-point bending, delamination, and temperature. During compression testing, when a local indentation 4 mm deep was introduced, the highest resistance change was recorded closer to the measurement channel due to defects in the conductive nanoparticle network. The delamination tests showed that, as the upper glass fibre layer was gradually separated from the honeycomb, the composite resistance increased exponentially. Three-point bending tests revealed a correlation between resistance change and deflection size. As the temperature varied from –14 °C to +70 °C, the relative resistance of the composite decreased by 50%. Furthermore, the study presents a 13 × 27 cm composite wing prototype equipped with six measurement channels connected to an “Arduino Nano ESP32“ microcontroller. The system measures electrical resistance in the channels in real-time and transmits the data wirelessly via Wi-Fi to the “Arduino IoT Cloud Remote” mobile application, where damage initiation and propagation can be graphically monitored. This technology enables the identification of honeycomb damage, fibre delamination, local cracks, and indentations
High-performance protocol for ultra-short DNA sequencing using Oxford Nanopore Technology (ONT) /
No full textIn recent years, Oxford Nanopore Technologies (ONT) has gained substantial attention across various domains of nucleic acid research, owing to its unique advantages over other sequencing platforms. Originally developed for long-read sequencing, ONT technology has evolved, with recent advancements enhancing its applicability beyond long reads to include short, synthetic DNA-based applications. However, sequencing short DNA fragments with nanopore technology often results in lower data quality, likely due to the absence of protocols optimised for these fragment sizes. To address this challenge, we refined the standard ONT library preparation protocol to improve its performance for ultra-short DNA targets. By utilising the same core reagents required for conventional ONT workflows, we introduced targeted alterations to enhance compatibility with shorter fragment lengths. We then benchmarked these adjustments against libraries prepared using the standard ONT protocol. Here, we present a comprehensive, step-by-step protocol that is accessible to researchers of various technical expertise, facilitating high-quality sequencing of ultra-short DNA fragments. This protocol represents a significant improvement in sequencing quality for short DNA sequences using ONT technology, broadening the range of possible applications
A study of the application of machine learning to answering and generating questions in lithuanian.
No full textThis project presents a system for automatic question generation and answering using natural language processing (NLP) methods in the Lithuanian language. The system is designed to support educational and business processes, allowing users to generate questions and answers using either pre-trained or custom fine-tuned artificial intelligence models. The literature review analyzes existing methods for question generation and answering, their application to low-resource languages such as Lithuanian, and compares model architectures, datasets, and evaluation metrics. The experimental part describes datasets created using automated data augmentation methods and presents training and evaluation experiments. The experiments compare models trained solely on the original data and models trained using augmented datasets. The results show that augmentation methods significantly improve question answering and question generation model performance. The best results were achieved using the back-translation technique
Research of control algorithms for grid-forming inverters.
No full textAs synchronous generation is gradually phased out and replaced by renewable energy sources (RES), the electricity system loses its inertia and resilience. Therefore, Grid-Forming (GFM) inverters are becoming a key technology to control the voltage and frequency of the grid and to provide inertia to the system. In this work, control algorithms for GFM inverters are investigated for the integration of RES into the power system. A GFM inverter model has been built in Matlab / Simulink environment, implementing three control strategies: droop, virtual synchronous machine (VSM) and synchronverter. The model was used to perform analyses of the optimal LC filter selection, model stability, short circuit resilience, inertia coefficient sensitivity and islanding mode. The study found that the optimal LC filter size for this model is L = 0.,2 mH, C = 800 µF, R = 0,097 Ω, after applying frequency analysis. The stability analysis showed that the stability of the GFM depends on the grid power and the static coefficient or damping coefficient of the active power control loop. The sensitivity analysis of the inertia coefficient showed that increasing the inertia constant decreases the amplitude of the peak overshoot and the Rate of Change of Frequency, but increases the transient time. Short-circuit and islanding tests showed that all three control strategies remain stable; however, the VSM displays the largest active-power overshoot yet settles in the shortest time. The results show that a properly selected LC filter and control parameters allow GFM converters to reliably maintain grid stability even at high RES levels
Forecasting the demand for renewable energy resources in Lithuania.
No full textWith the implementation of the European Green Deal in EU member states, including Lithuania, consumption of renewable energy is on the rise. Such shifts fundamentally transform the structure of national energy sectors, shape the priorities of legislative and executive authorities, create new opportunities for member states and alter their relative advantages. For Lithuania, this initiative also presents the opportunity to become a strategically important country, a net exporter of green energy. Therefore, it is crucial to understand the overall trend in the demand for renewable energy resources within the country and how it will evolve in the upcoming years. Future trends are forecasted using various mathematical methods: time-series models, econometric models, ARIMA-family models, grey models and deep-learning models. External cross-validation is employed to assess model accuracy, while hyperparameters are tuned via internal cross-validation. Models within each family are selected by experimenting with different alternatives and modifications according to uniform error metrics (MAPE, MAE, RMSE). The exponential grey model EXGM (1,1) proved to be the best model for forecasting the available dataset
Determination of the chemical composition of essential oils and evaluation of biological activity and use in the production of an alternative cleaner against pathogens.
No full textThe increasing resistance of pathogens to antibiotics and the risks to human health posed by synthetic cleaners have prompted the search for alternative, safer and bioactive substances. The aim of this work was to investigate the potential of essential oils as active agents in alternative surface cleaners, particularly for the control of microbiological contamination in laboratory and healthcare environments. In this work, the chemical composition of the essential oils of the patchouli (lot. Pogostemon cablin Benth.), orange lemon (lot. Citrus sinensis L.) and Texas cedar (lot. Juniperus mexicana Schiede.) was analyzed by gas chromatography and mass spectrometry, and the main active compounds identified. The main components of true patchouli are patchouli alcohol 25.47 %, α-guaiene 15.12 %, α-bulnesene 18.84 %. The main constituents of Texas cedarwood essential oil are: thujopsene 34.99 %, cedrol 20.52 % and α-cedrene 15.83 %. The main constituent of orange lemon essential oil is limonene 95.37 %. The antibacterial and fungicidal activity of the essential oils against pathogens has been evaluated and tested. Due to the low antimicrobial activity of Texas cedarwood essential oil, it was not used for further studies. Based on the results obtained for the essential oils and the literature analysis, alternative cleaning agents were formulated and their effects against pathogens were verified experimentally. The studies showed that the surfactants in the cleaner synergistically contributed to the enhanced inhibition of pathogens by the essential oils of pathogenic patchouli and orange lemon tree. Bacterial and yeast cultures showed a strong increase in pathogen susceptibility. The results of the study suggest that this type of cleaners may be useful in reducing microbial contamination of surfaces and the spread of infections, while minimizing the health and environmental risks of chemical use
Methodology for requirements synchronization in Agile projects by aggregating requirements from different sources.
No full textConsistent synchronization of project requirements is crucial for achieving successful project outcomes. However, when using Agile methodology in project management, a key challenge arises since there is no standardized process for requirements engineering. As a result, the handling of requirements relies heavily on the competencies and work practices of individual team members. Although numerous project management software tools exist, establishing a consistent and unified structure for requirements remains possible. Unfortunately, these tools are often not fully optimized to manage the continuous changes that occur in project requirements, such as modifications, merging, deletion, addition of new requirements, and identification of overlapping requirements. Due to these limitations, teams involved in project execution frequently encounter issues with improper synchronization of requirements, leading to miscommunication and misalignment throughout the project lifecycle. The objective of this master’s thesis is to establish a methodology for consistent requirements synchronization in Agile projects by proposing an aggregation of requirements from multiple sources. This methodology offers clear guidelines on structuring requirements effectively by integrating data from various platforms, including Jira, Excel, email, Word, and other tools. It also ensures the periodic updating of requirements throughout Agile project execution and provides mechanisms for resolving conflicts due to inconsistent requirements. The proposed methodology was tested in three projects, and feedback was collected through a survey involving 10 participants who directly engaged with these projects. The results confirmed the initial hypotheses, demonstrating that the developed methodology is both effective and well-received. Implementing a consolidated approach to requirements synchronization significantly contributes to achieving successful project outcomes